Investigation of Data-driven Modelling and Feedforward Control for a Two Speed Magnetorheological Fluid Dual Clutch Transmission of Electric Vehicles
Publication Name
IET Conference Proceedings
Abstract
This paper presents data-driven modelling and control of a two-speed magnetorheological fluid dual-clutch transmission (MRFDCT) developed for electric vehicles (EVs). Because of the intricate nonlinear dynamics and rate-dependent hysteresis, an accurate mathematical model for this transmission system remains a challenge. The study investigates a purely data-driven modelling technique called Dynamic Mode Decomposition with Control (DMDc) to explore the correlation between the input currents and output torques of the MRFDCT system. DMDc is a mathematical model-free modelling strategy that generates linear state space models for intricate nonlinear dynamical systems based on input and output data. In this study, DMDc was used to develop linear state space models for the input currents and output torques under several scenarios: sinusoidal and step responses of the upshifting process. Under different scenarios, the associated linear state-space models generated by DMDc are able to accurately predict experimental results, validating their effectiveness. The inverse models of MRFDCT were constructed by utilising the same DMDc method and reversing the input current and output torque. Based on the obtained state space models of the inverse models for gear 1 and gear 2, a feedforward proportional-integral-derivative (PID) control strategy is proposed for controlling torque. This strategy is found to be effective when the outputs of the feedforward PID controller are matched to reference torques.
Open Access Status
This publication is not available as open access
Volume
2023
Issue
26
First Page
66
Last Page
72
Funding Number
LP190100603
Funding Sponsor
Hawai'i Sea Grant, University of Hawai'i